2020 21st International Symposium on Quality Electronic Design (ISQED)最新文献_第7页

Proceedings of the Twentyfirst International Symposium on Quality Electronic Design 第21届质量电子设计国际研讨会论文集

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/isqed48828.2020.9137030

引用次数: 0

Accurate and Efficient Quantized Reservoir Computing System 准确高效的量化储层计算系统

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/ISQED48828.2020.9136986

Shiya Liu, Yibin Liang, Victor Gan, Lingjia Liu, Y. Yi

{"title":"Accurate and Efficient Quantized Reservoir Computing System","authors":"Shiya Liu, Yibin Liang, Victor Gan, Lingjia Liu, Y. Yi","doi":"10.1109/ISQED48828.2020.9136986","DOIUrl":"https://doi.org/10.1109/ISQED48828.2020.9136986","url":null,"abstract":"Quantization is a widely used technique to deploy deep learning models on embedded systems since this technique could reduce the model size and computation dramatically. Many quantization approaches have been proposed in recent years. Some quantization approaches are aggressive which could sufficiently reduce the model size and computation. However, the accuracy could be significantly decreased. To resolve this issue, some research groups have proposed smoother approaches to reduce accuracy loss. However, smoother approaches would use much more resources than aggressive approaches. In our work, we proposed a quantization approach which reduces resource utilization dramatically without losing much accuracy. We have successfully applied our quantization approach to the reservoir computing (RC) system. Compared to the RC system using floating-point numbers, our proposed RC system reduces the resource utilization of BRAM, DSP, Flip-Flop (FF) and Lookup Table (LUT) by 47%, 93%, 93%, and 87%, respectively, while only loses 0.08% accuracy on the NARMA10 dataset. Meanwhile, our proposed RC system uses approximately 45%, 14%, and 21% less BRAM, FF, and LUT respectively than the quantized RC system using other popular quantization approach.","PeriodicalId":225828,"journal":{"name":"2020 21st International Symposium on Quality Electronic Design (ISQED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131378960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Proceedings-ISQED 2020

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/isqed48828.2020.9137031

引用次数: 0

Integrated CAM-RAM Functionality using Ferroelectric FETs 利用铁电场效应管集成CAM-RAM功能

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/ISQED48828.2020.9136998

Sumitha George, N. Jao, A. Ramanathan, Xueqing Li, S. Gupta, J. Sampson, N. Vijaykrishnan

引用次数: 2

[Embedded Tutorials - 4 abstracts] [嵌入式教程- 4个摘要]

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/isqed48828.2020.9137009

引用次数: 0

Self-Powered IOT System for Edge Inference 用于边缘推理的自供电物联网系统

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/isqed48828.2020.9137027

Dileep Kurian, T. Karnik, Saksham Soni, Saransh Chhabra, Suhwan Kim, Jaykant Timbadiya, Ankit Gupta, K. Ravichandran, Mukesh Bhartiya, Angela Nicoara

引用次数: 0

ISQED Proceedings

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/isqed48828.2020.9136995

引用次数: 0

Integrated Implantable Electrode Array and Amplifier Design for Single-chip Wireless Neural Recordings 集成植入电极阵列和放大器设计的单片机无线神经记录

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/ISQED48828.2020.9136982

H. Shan, N. Conrad, S. Ghotbi, J. Peterson, S. Mohammadi

引用次数: 1

An Efficient Deep Reinforcement Learning Framework for UAVs 一种高效的无人机深度强化学习框架

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/ISQED48828.2020.9136980

Shangli Zhou, Bingbing Li, Caiwu Ding, Lu Lu, Caiwen Ding

引用次数: 9

Improving Reliability of Quantum True Random Number Generator using Machine Learning 利用机器学习提高量子真随机数生成器的可靠性

2020 21st International Symposium on Quality Electronic Design (ISQED) Pub Date : 2020-03-01 DOI: 10.1109/ISQED48828.2020.9137054

Abdullah Ash-Saki, M. Alam, Swaroop Ghosh

{"title":"Improving Reliability of Quantum True Random Number Generator using Machine Learning","authors":"Abdullah Ash-Saki, M. Alam, Swaroop Ghosh","doi":"10.1109/ISQED48828.2020.9137054","DOIUrl":"https://doi.org/10.1109/ISQED48828.2020.9137054","url":null,"abstract":"Quantum computer (QC) can be used as a true random number generator (TRNG). However, various noise sources introduce a bias in the generated number which affects the randomness. In this work, we analyze the impact of noise sources e.g., gate error, decoherence, and readout error in QC-based TRNG by running a set of error calibration and quantum tomography experiments. We employ a hybrid quantum-classical gate parameter optimization routine to find an optimal gate parameter. The optimal parameter compensates for error-induced bias and improves the quality of the random number by exploiting even the worst quality qubits. However, searching the optimal parameter in a hybrid setup requires time-consuming iterations between classical and quantum machines. We propose a machine learning model to predict optimal quantum gate parameters based on the qubit error specifications. We validate our approach using experimental results from IBM's publicly accessible quantum computers and the NIST statistical test suite. The proposed method can correct bias in any worst-case qubit by up to 88.57% in real quantum hardware.","PeriodicalId":225828,"journal":{"name":"2020 21st International Symposium on Quality Electronic Design (ISQED)","volume":"285 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124252485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7